def start():
# create config dict
config = config_init(argv)
q_from_main_to_listener = Queue()
# Debug mode uses LSL_Generator for debuging
if config['general'].getboolean('debug_mode'):
print('Debug Mode!!!')
debug_time = config['general'].getint('debug_time')
lsl_stream_generator_path = config['paths'][
'lsl_stream_generator_path']
import importlib.util
spec = importlib.util.spec_from_file_location(
"lsl_stream_generator", lsl_stream_generator_path)
lsl_stream_generator = importlib.util.module_from_spec(spec)
spec.loader.exec_module(lsl_stream_generator)
lsl_stream_debug = lsl_stream_generator.LSL_Generator(
debug_time, 69, 2048, q_from_main_to_listener)
lsl_stream_debug.start()
time.sleep(2)
# Queue is used to pass arguments from display thread to main thread (to LSL_Listener)
q_from_main_to_listener.put(('lsl_stream_listener_state', True))
q_from_main_to_listener.put(('patient_state', 1))
lsl_listener = LSL_Listener(config, 2048, q_from_main_to_listener)
time.sleep(0.5)
record_time = 20
# initialise thread for display
thread = Thread(target=update, args=(q_from_main_to_listener, record_time))
thread.start()
lsl_listener.record_using_buffer()
def main():
setpriority(pid=None, priority=5)
config = config_init(sys.argv)
# Debug mode uses LSL_Generator for debuging
if config['general'].getboolean('debug_mode'):
print('Debug Mode!!!')
sys.path.append(config['paths']['lsl_stream_generator_path'])
sys.path.append(config['paths']['lsl_stream_generator_path'] +
'/pynfb')
from generators import run_eeg_sim
freq = config['amp_config'].getint('fs_amp')
name = config['amp_config']['lsl_stream_name_amp']
labels = [
'channel{}'.format(i)
for i in range(config['amp_config'].getint('n_channels_amp'))
]
lsl_stream_debug = lambda: run_eeg_sim(freq, name=name, labels=labels)
lsl_stream_debug_tread = Thread(target=lsl_stream_debug, args=())
lsl_stream_debug_tread.daemon = True
lsl_stream_debug_tread.start()
print("generators.run_eeg_sim start DEBUG LSL \"{}\"".format(
config['amp_config']['lsl_stream_name_amp']))
print(
'Running application, please check:\n 1) PN connection\n 2) Amplifier connection'
)
# initiate stream of PN and Amp data
pnhandler = PNHandler(config)
pnhandler.start()
inlet_amp = get_inlet_amp(config)
time.sleep(1.5)
# record train data
if config['general'].getboolean('record_enable'):
experiment_record = ExperimentRecord(config, pnhandler, inlet_amp)
experiment_record.record_data()
input("Data is recorded, press Enter to continue...")
# start realtime experiment
if not config['general'].getboolean('realtime_enable'):
return
# stimulate during realtime
stimulator = Stimulator(config)
stimulator.connect()
#
try:
experiment_realtime = ExperimentRealtime(config, pnhandler, inlet_amp,
stimulator)
experiment_realtime.fit()
experiment_realtime.decode()
finally:
#experiment_realtime.stop()
stimulator.close_connection()
def main():
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%a %d %b %Y %H:%M:%S')
if not config.config_init():
logger.fatal(
"config init failed , please check above log and rerun again")
time.sleep(10)
return -1
while True:
try:
result = update_domain_A_record()
if result:
break
else:
time.sleep(10)
except Exception as ex:
logger.fatal(ex)
time.sleep(10)
user_alive_time = time.time()
str_user_alive_time = time.asctime()
while True:
try:
cnt = get_current_login_user()
if cnt is None or cnt > 0:
user_alive_time = time.time()
str_user_alive_time = time.asctime()
if user_alive_time + config.MAX_IDLE_TIME < time.time():
logger.info(
f"idle time({config.MAX_IDLE_TIME}) reached since last user login time is "
f"({str_user_alive_time}).We'll close the instance by name ({config.INSTANCE_NAME})"
)
ali_instance.stop_instance_by_name(config.INSTANCE_NAME)
break
else:
time.sleep(60)
except Exception as ex:
logger.fatal(ex)
time.sleep(60)
while True:
logger.info("waiting server to die...")
time.sleep(60)
def start():
# create config dict
config = config_init(argv)
# Queue is used to pass arguments from display thread to main thread (to LSL_Listener)
q_from_display_to_recorder = Queue()
# remove_mode does not use data from lsl, just show pictures
if config['general'].getboolean('show_objects_mode') or config[
'general'].getboolean('show_actions_mode'):
patient_display = Display(config, q_from_display_to_recorder)
patient_display.start()
return
# Debug mode uses LSL_Generator for debuging
if config['general'].getboolean('debug_mode'):
print('Debug Mode!!!')
debug_time = config['general'].getint('debug_time')
lsl_stream_generator_path = config['paths'][
'lsl_stream_generator_path']
import importlib.util
spec = importlib.util.spec_from_file_location(
"lsl_stream_generator", lsl_stream_generator_path)
lsl_stream_generator = importlib.util.module_from_spec(spec)
spec.loader.exec_module(lsl_stream_generator)
lsl_stream_debug = lsl_stream_generator.LSL_Generator(
debug_time, 69, 2048, q_from_display_to_recorder)
lsl_stream_debug.start()
time.sleep(2)
# create LSL_Listener object
recorder = Recorder(config, q_from_display_to_recorder)
# Activate Display if not debugging or if debugging with stream from LSL_Generator
#if not config['general'].getboolean('debug_mode') or config['general'].getboolean('lsl_outlet_random'):
patient_display = Display(config, q_from_display_to_recorder)
patient_display.start()
time.sleep(0.5)
# Type data saving
recorder.record()
# process data and plot results
decoder = Decoder(config)
decoder.process_current_file()
if datasets:
dataset_stacked = np.vstack(datasets)
new_length = dataset_raw_data.shape[0] + dataset_stacked.shape[0]
dataset_raw_data.resize(new_length, axis = 0)
dataset_raw_data[-dataset_stacked.shape[0]:] = dataset_stacked
for i in range(1, len(file[group].keys())):
del file[group]['raw_data{}'.format(i)]
if __name__ == '__main__':
config = config_init([''])
recorder = Recorder(config)
recorder.reforge_into_raw_data()
add = 1
if add:
a = np.random.random(size=(3,72))
for i in range(2):
recorder.save_data_rest(a)
for i in range(6):
recorder.save_data_actions(a)
with h5py.File(save_data.experiment_data_path, "r") as file:
keys = file.keys()
print(keys)
for key in keys:
if key != 'fs':
# Suppress warnings
import warnings
warnings.filterwarnings("ignore")
# For developer mode only
if __name__ == '__main__':
# Set base directory
config.set_base_dir(os.path.dirname(os.path.realpath(__file__)))
# Set resource directory
config.set_resource_dir()
# Initialize configurations
config.config_init()
from utils import bgcolors
GLOBAL_HOST = config.get_config('camera_database', 'GLOBAL_HOST')
GLOBAL_USER = config.get_config('camera_database', 'GLOBAL_USER')
GLOBAL_PWD = config.get_config('camera_database', 'GLOBAL_PWD')
class CameraDatabase():
def __init__(self,
db_name,
db_table,
host=GLOBAL_HOST,
user=GLOBAL_USER,
password=GLOBAL_PWD,
class Saver():
def __init__(self, config):
self.config = config
# create Path to experiment_data file
self.experiment_data_path = Path(self.config['paths']['experiment_data_path'])
self.dataset_width = self.config['saver'].getint('dataset_width')
# create h5 experiment_data file with empty groups of nonfixed length and group with fs
self.groups = self.config['saver']['group_names'].split(' ')
if not self.experiment_data_path.is_file():
with h5py.File(self.experiment_data_path, 'a') as file:
for group in self.groups:
file.create_dataset(group + '/raw_data', (0, self.dataset_width), maxshape=(None, self.dataset_width))
file.create_dataset('fs', data=np.array(self.config['general'].getint('fs')))
# save chunk of data into new raw_data# dataset
def save_data_buffer(self, data, data_type):
with h5py.File(self.experiment_data_path, "a") as file:
keys = file[data_type].keys()
dataset_name = '{}/raw_data{}'.format(data_type, len(keys))
file.create_dataset(dataset_name, shape=data.shape, data=data)
def save(self):
# after saving into file with raw_data# chunks remake into single raw_data
def reforge_into_raw_data(self):
with h5py.File(self.experiment_data_path, "a") as file:
for group in self.groups:
dataset_raw_data = file[group]['raw_data']
datasets = []
if len(file[group].keys()) > 1:
for i in range(1, len(file[group].keys())):
dataset = file[group]['raw_data{}'.format(i)][()]
datasets.append(dataset)
if datasets:
dataset_stacked = np.vstack(datasets)
new_length = dataset_raw_data.shape[0] + dataset_stacked.shape[0]
dataset_raw_data.resize(new_length, axis = 0)
dataset_raw_data[-dataset_stacked.shape[0]:] = dataset_stacked
for i in range(1, len(file[group].keys())):
del file[group]['raw_data{}'.format(i)]
if __name__ == '__main__':
config = config_init([''])
recorder = Recorder(config)
recorder.reforge_into_raw_data()
add = 1
if add:
a = np.random.random(size=(3,72))
for i in range(2):
recorder.save_data_rest(a)
for i in range(6):
recorder.save_data_actions(a)
with h5py.File(save_data.experiment_data_path, "r") as file:
keys = file.keys()
print(keys)
for key in keys:
if key != 'fs':
print(key)
print(file[key].keys())
print(key, file[key]['raw_data'].shape)
for i in range(1, len(file[key].keys())):
print(key, file[key]['raw_data{}'.format(i)].shape)
def prepare(self, X, Y, rotate):
if rotate:
self._rotate_resize(X, Y)
self._pad(X, Y)
def get_img(self):
return self.img
def get_file_name(self):
return self.file_name
def get_number(self):
return int(self.file_name[:-4])
def get_type(self):
return self.picture_type
def shape(self):
return self.img.shape
if __name__ == '__main__':
from queue import Queue
q = Queue()
from config import config_init
argv = []
config = config_init(argv)
d = Display(config, q)
d.start()
# d._show_image(d.image_button_any, 5000)